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Due to the high penetration of renewable energy resources in microgrids (MGs), the grid inertia becomes low which leads to the grid to be vulnerable to large disturbances. The energy storage devices can play an important role to enhance the inertia of MGs. However, due to the high investment cost of storages or their dp/dt limitation, the installed energy storages cannot cover the challenge of high df/dt. A prominent solution to solve the problem is to use the inertia response of the wind generators. However, relatively high second frequency nadir is the main drawback of using the inertia response of the wind generators which may impose an extensive disturbance to MGs. Accordingly, a coordinated operation strategy for MGs between wind generator and hybrid energy storage (HES) system is proposed in this paper. In addition, to improve the inertia response of the MG; providing high-quality communication infrastructures with low delay and increasing the Ultracapacitor capacity have been paid attention. In this paper, the costs of the installed Ultracapacitor and quality of communication services are defined as the operation cost. Guaranteeing enough frequency damping for the MG with low operation cost are two conflict objectives. Therefore, a multi-objective optimization method is used to set the controllers’ values and reduce the operation cost. The results confirmed that the effectiveness of the proposed strategy to control hybrid power storage in coordination with the wind generator and the frequency recovery process is improved. Also, employing the optimum values guaranteed the frequency damping effectively with low operation cost. The Integral Absolute Error (IAE) value and operation cost are reduced by 13.6% and 32%, respectively. Also, the simulation results show that the maximum MG frequency deviation and maximum df/dt is well compatible with different standards in the presence of load perturbations and different wind speeds.

Lifetime estimation of power semiconductor devices have been widely investigated to improve the reliability and reduce the cost of maintenance of power converters. However in most reported work, the aging effect is not considered in the lifetime evaluation process due to the omission or limitation of thermal cycle counting method. Additionally, the electrical/thermal simulation and lifetime estimation are usually implemented in different simulators/platforms, for the same reason. Thus, to tackle these problems, a concise but comprehensive MOSFET model that enables electro-thermal modeling, aging and lifetime estimation on LTspice® circuit simulator is proposed in this paper. The idea comes from the fact that, MOSFET on-state resistance Rds,on is not only temperature dependent, but also widely accepted as the device failure precursor. In other words, as it carries critical information about instantaneous temperature and aging progress. Hence, co-simulation can be achieved by constructing electrical, thermal, and aging and lifetime sub-modules exclusively first, and using Rds,on, to build linkages among them. Averaged modeling technique is adopted due to the ease of establishing links among these three sub-modules, and fast simulation speed as compared to a switched converter model. Behavioral models are employed to realize the thermal cycles counting, stress accumulation and degradation evaluation. This paper demonstrates that it is possible to use a single simulation software to monitor performances of devices and circuits, and their lifetime estimation simultaneously. High-stress thermal cycling and long-term random mission profiles are applied to verify the correctness of the model and to mimic a 10-year load respectively. An accelerated aging trend can be observed in the long-term mission profile simulation, which is in agreement with the theory. Facilitated by the employment of averaged circuits, the proposed method is a good simulation/analytical tool to implement a long-term mission profile that requires reliability assessment.

Made available in DSpace on 2022-04-29T08:29:33Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Distribution network is an essential part of electric power system, which however has higher power losses than transmission system. Distribution losses directly affect the operational cost of the system. Therefore, power loss reduction in distribution network is very important for distribution system users and connected customers. One of the commonly used ways for reducing losses is distribution system reconfiguration (DSR). In this process, configuration of distribution network changes by opening and closing sectional and tie switches in order to achieve the lowest level of power losses, while the network has to maintain its radial configuration and nodal voltage limits, and supply all connected loads. The DSR aiming loss reduction is a complex mixed-integer optimization problem with a quadratic term of power losses in the objective function and a set of linear and non-linear constraints. Accordingly, distribution network researchers have dedicated their efforts to developing efficient models and methodologies in order to find optimal solutions for loss reduction via DSR. In this paper, an efficient mathematical model for loss minimization in distribution network reconfiguration considering the system voltage profile is presented. The model can be solved by commercially available solvers. In the paper, the proposed model is applied to several test systems and real distribution networks showing its high efficiency and effectiveness for distribution systems reconfiguration. Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira Department of Electrical Power Engineering Tishreen University School of Electrical and Computer Engineering National Technical University of Athens Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Associated Laboratories Institute of Bioenergy Research (IPBEN) São Paulo State University Campus of Ilha Solteira

The increasing number of megawatt-scale photovoltaic (PV) power plants and other large inverter-based power stations that are being added to the power system are leading to changes in the way the power grid is operated. In response to these changes, new grid code requirements establish that inverter-based power stations should not only remain connected to the grid during faulty conditions but, also provide dynamic support. This feature is referred in the literature to as momentary cessation operation. The few published studies about momentary cessation operation for PV power plants have not shed much light on the impact of these systems on the overall power system stability problem. As an attempt to address this issue, this paper proposes a control scheme for PV inverters that improves the transient stability of a synchronous generator connected to the grid. It is shown through the paper that the proposed control scheme makes the PV inverter’s dc link capacitors absorb some of the kinetic energy stored in the synchronous machine during momentary cessation. Besides that, the proposed solution is also able to improve voltage stability through the injection of reactive power. Experimental and simulation results are presented in order to demonstrate the effectiveness of the proposed control scheme.

In response to national and international carbon reduction goals, renewable energy resources like photovoltaics (PV) and wind, and energy storage technologies like fuel-cells are being extensively integrated in electric grids. All these energy resources require power electronic converters (PECs) to interconnect to the electric grid. These PECs have different response characteristics to dynamic stability issues compared to conventional synchronous generators. As a result, the demand for validated models to study and control these stability issues of PECs has increased drastically. This paper provides a review of the existing PEC model types and their applicable uses. The paper provides a description of the suitable model types based on the relevant dynamic stability issues. Challenges and benefits of using the appropriate PEC model type for studying each type of stability issue are also presented.

This paper considers the economy and reliability of the microgrid cluster system, and proposes a bi-level optimized operation strategy for the microgrid cluster, which aims to improve the economic benefits of the microgrid cluster and reduce the operating risk of microgrid. The upper layer takes the microgrid cluster as the research object, with the goal of minimizing the total operating cost of the system, and establishes the optimal model of the microgrid cluster. The lower layer takes the sub-microgrid as the research object, and establishes the microgrid optimization model with the goal of optimal operating cost and operating risk index. The method of combining mixed integer linear programming and IABC algorithm is used to solve the multi-objective bi-level optimization model, and to provide decision makers with two microgrid dispatching schemes with different focuses. Finally, an example is used to verify the effectiveness of the bi-level optimization strategy and improved algorithm proposed in this paper, as well as the practicability of the two dispatching schemes.

It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

Due to the lack of inertial support of the physical rotor of reliable power, the problem of weak inertia of the system is prominent when large-scale grid connection. Aiming at the weak inertia problem of new energy power systems, this paper takes the photovoltaic power generation system with generalized droop control as the research object. Firstly, the paper introduces the structure of the photovoltaic power generation system and the mathematical principle of generalized droop control At the same time, the electrical torque model of the system is established in order to reveal the inertia characteristics of the grid-tied inverter under the generalized droop control, moreover, the influencing factors of the system’s inertia characteristics and the influence rules of parameters are analyzed and studied. Finally, simulation analysis verifies the correctness of the analysis. The research results show that the photovoltaic inverter system under the generalized droop control has obvious inertia characteristics, the change of inertia size is affected by the dominant zero pole of the controller, and the equivalent inertia coefficient has frequency domain characteristics.

In this paper, a new high efficiency soft-switching non-isolated three-port converter (TPC) is proposed. In the conventional TPC, three switches are required to process power in different directions between inputs and output. Providing soft-switching condition for all switches using a low number of auxiliary components is a challenging task. In this paper, the conventional TPC topology is modified by changing its structure and adding a simple auxiliary circuit such that all switches operate under soft-switching condition in all operating modes. To reduce the volume of the converter, the magnetic components are integrated using a coupled inductor in the auxiliary soft-switching cell. In this paper, various converter operating modes are presented, and design considerations are discussed. Finally, a prototype 200 W, 100 V converter is implemented in the laboratory, and the theoretical analysis is validated by the experimental results.

In this paper, an extensive reliability analysis of wind energy conversion systems (WECS) is presented. Elaborate the analysis is presented starting from the subassembly level to the subsystem level, then the system or the overall WECS. The fault tree method with a Weibull probability distribution function is introduced as a complete model for estimating the wind turbine subassemblies’ reliability. The model was tested using a massive dataset of failure rates of various wind turbine subassemblies derived from relevant literature, comprising various operating concepts and the different climate conditions. In addition, ranking for various subassemblies of wind energy conversion systems concerning their impact on the overall system reliability is also presented in this paper to identify the weak items and subsystems. This identification guides the designers and planners in setting the appropriate maintenance strategies to increase the overall reliability of the considered systems and achieve a desired level of reliability. The results indicate that the model has practical applications for managing wind turbines, and the implementation demonstrates the proposed approach’s effectiveness and efficiency, which may significantly enhance the WECS reliability.

Circuit breaker (CB) and fault current limiter (FCL) are two major fault protection devices used in mesh-type DC microgrids. This paper proposes the parameters selection method to obtain a reliable and economic configuration scheme of CBs and FCLs applied in mesh-type DC microgrid. The breaking time of CB, the nominal breaking current of CB and the inductance of FCL are selected as three optimization parameters in an evaluation model. Since the reliability of fault protection scheme depends on whether the nominal breaking current of the CB is greater than the line current at the breaking moment of CB, it is necessary to accurately calculate the post-fault line current. Based on this, an accurate transient DC microgrid model is presented in this paper. And then, an improved non-dominated sorting genetic algorithm II (NSGA-II) is used to configure the parameters of CBs and FCLs quickly. Besides, a case study on six-terminal DC microgrid test system shows that the proposed method can obtain accurate post-fault line current and can achieve the parameters selection of CBs and FCLs.

Wireless charging is a convenient method of charging Electric Vehicles (EVs). Its application has been widely studied for electric cars but so far there have been limited experiments for electric bicycles (e-bikes). This paper addresses the design process and implementation details related to a wireless charger for a specific e-bike. Some issues such as the position of the secondary coil or the control algorithm prove to be crucial to the performance of the wireless transfer. Concerning the placement of the secondary coil, the paper proposes and validates a theoretical model to quantify the effects of the surrounding interfering materials. A CC-CV (Constant Current - Constant Voltage) control algorithm has been implemented to validate the theoretical results in these two charging phases. The control, the coils and the compensation networks have been designed to be simple and robust. The theoretical model, the charger and its control have proven to be effective in a real 84-W e-bike.

As a result of the increase in penetration of inverter-based generation such as wind and solar, the dynamics of the grid are being modified. These modifications may threaten the stability of the power system since the dynamics of these devices are completely different from those of rotating generators. Protection schemes need to evolve with the changes in the grid to successfully deliver their objectives of maintaining safe and reliable grid operations. This paper explores the theory of traveling waves and how they can be used to enable fast protection mechanisms. It surveys a list of signal processing methods to extract information on power system signals following a disturbance. The paper also presents a literature review of traveling wave-based protection methods at the transmission and distribution levels of the grid and for AC and DC configurations. The paper then discusses simulations tools to help design and implement protection schemes. A discussion of the anticipated evolution of protection mechanisms with the challenges facing the grid is also presented.

The power-gas integrated energy system lays a foundation for improving energy efficiency, promoting the large-scale utilization of renewable energy, and building a low-carbon sustainable energy system. However, the complex interaction between the power system and the natural gas system has brought about prominent, more diverse, and complex dynamic stability problems. Interference in one system will propagate to another system through coupling devices, which may cause serious disasters such as cascading failures. In order to analyze the transient process of the natural gas system caused by the adjustment of the coupling devices, this paper established a model for the coupling devices and the dynamic process of the natural gas system in the power-gas integrated energy system. By comparing the existing numerical calculation methods, the method of characteristic was selected to solve the hyperbolic partial differential equations, and the mathematical models of different nodes in the natural gas system were established. The accuracy and practicability of the algorithm are verified by 3 cases, and the accuracy of the algorithm is verified by case 1 and case 2 including the power-to-gas(P2G)and gas turbine. Case 3 verified the practicability of the algorithm by analyzing the fluctuation of the natural gas system caused by the adjustment of power-gas coupling links, and the adjustment is caused by the change of new energy generation. The results of this paper can analyze the interaction of coupling equipment and natural gas system, and lay the foundation for the realization of dynamic simulation of the whole integrated energy system.

This paper introduces a new method of estimating the variation interval and the expected value of optimal economic operation cost for the energy hub (EH) model considering load and renewable energy uncertainties in the residential district. Firstly, renewable energy and load uncertainties are defined as uncertain sets of historical data. Due to dispatch factors, the traditional EH model is non-linear and difficult to calculate and analyze. In this paper, a multi-step modeling method is developed for the breakdown of a complex EH model (CEH) into several simple EH (SEH) models and variable substitutions in each SEH, to eliminate the non-linearity of CEH. The upper and lower bound of the operating costs are then obtained based on the duality theorem and the expected value is calculated by expected value model. Finally, the case study in Beijing residential district is carried out to confirm the efficiency of the proposed method.

Phase-locked loop (PLL) systems play a crucial role in grid synchronization. Since variations on the grid voltage parameters are in general uncertain, the dynamics of PLLs are by nature nonlinear. This fact imposes a relevant challenge to guarantee their stability. Unfortunately, as argued in this paper, there is a remarkable lack of global stability tests even for the most common approaches, such as the synchronous reference frame (SRF). In this case, there is only available a small-signal—linearized—model-based approach to stability. The main issue of resorting to local conclusions is that synchronization is forced to operate under conservative conditions, where large-signal variations are forbidden. Needless to say, this has a detrimental impact on applications, since performance is limited by small disturbance assumptions. Motivated by this problem, in the present work, we provide global asymptotic stability tests for two fundamental PLL algorithms. Namely, the conventional SRF-PLL and the enhanced SRF-EPLL. To do so, we use a Lyapunov approach acting directly on nonlinear trajectories. Consequently, the provided stability proofs do not rely on any linearization technique, frequency-domain conversion, or small-signal model representation. The main contribution in this paper is a set of stability conditions that are valid in a global sense. Moreover, it is shown that these results have direct practical implications in the form of new robust PLL gain tuning guidelines.

Solar energy technology has gained significant attention in recent years. It has strongly emerged as an alternative to the conventional mode of electricity generation for developing countries like India for their rising energy needs. Solar energy based electricity generation not only addresses environmental concerns but also increases the energy security and reduces the carbon footprints generated by the coal-based power plant. To boost the share of Renewable energy in the energy mix, the renewable energy policy introduced by the government has acted as a catalyst for large scale deployment of solar energy system to push it as the major energy alternative for India. The normal intensity of solar radiation in India is 220 MW per km square with around 300 sunny days in a year, whereas the coal-based plant generates a huge amount of carbon footprints. Therefore, solar based power generation has the potential of not only reducing the emissions and but also it can cater the rising energy demand. Thus solar photovoltaic plants has become a major alternative energy option in India with the ability to give an improved power supply, reduced global warming and enhance the security of energy supply. In this paper, efforts have been made to look into the overall perspective of solar energy and coal based generation by studying and analyzing the present status, technologies use, accessibility, policies interventions, major contributions, future potentials and trends of solar energy and fossil fuels in the Indian power sector. A feasibility analysis of solar technology implementation with the replacement of coal based plant pant is carried out for Singrauli, India. Results of the proposed study shows that the coal-based power plant generates a huge amount of carbon footprints and it can be reduced by replacing the coal based power plants with solar energy based power generation system. Consequently solar energy based power generation system is a key alternative for developing countries like India having metrological parameters similar to the case study done in this paper with the capacity to provide an improved supply, decreased global warming in addition to improving the energy security.

Integrating Distributed Energy Resources (DERs) and Micro-Grid (MG) into a system evolved the traditional power system. In spite of their significant advantages, MGs may result in volatility and uncertainty in the power systems. For reliable operation of the grid, energy trading among MGs should be optimized to maintain a fair trading price, maximize participants’ profit, and satisfy network constraints. In this paper, the optimal power trading among multiple reconfigurable MGs is formulated as a Mixed-Integer Nonlinear Programming (MINLP) considering all energy resources and their dynamic prices. In spite of the other methods in the literature, the proposed method minimizes the total cost (increase sales and decrease purchases) and transmission loss considering all energy resources in the MGs. In order to flatten the load profile, a time-based load profile is considered for the demand response program. The performance of the proposed model is evaluated on an IEEE 6-bus network as well as a modified IEEE 33-bus test system. The results verify that the proposed method, (i) determines the best configuration among MGs with a switching reduction of about 30%, (ii) optimizes the power generation of energy resources with 12% reduction in energy production, and (iii) optimizes the power trading costs with a 10% reduction in costs compared with the basic model without DR and trade that is introduced as $Scen.1$ in this paper.

The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81%, 28.58%, 1.1431%, 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications.

In order to improve the economy and safety of multi-microgrids (MMGs) scheduling, this paper proposes a research on MMGs scheduling strategy that takes into account dynamic electricity prices based on the blockchain. We first introduce the principle of blockchain, analyze the security and economy of the combination of blockchain and MMGs scheduling, and design the scheduling architecture and process based on the blockchain platform. Second, we set a dynamic electricity price model according to the total power supply and demand of MMGs, and set a load optimization model. Finally, we take optimal system economy and minimum environmental pollution as the objective function, then use the linear programming method and the improved krill herd algorithm (KHA) with nonlinear changes in weights to solve the problem. The simulation results show that: (1) The dynamic electricity prices can reflect the power supply and demand of microgrids and optimize the load; (2) Comparing the three scheduling schemes, the strategy in this paper can improve the economic and environmental protection of MMGs by 37.33% and 39.34%, while reduce the interactive power between the microgrid and the distribution network by 56.28%, and the curtailment rate by 63.22%; (3) The improved krill herd algorithm has higher convergence speed and convergence accuracy;(4) The blockchain technology can ensure the security of scheduling data.

There is a wide application prospect of Nearest level pulse width modulation (NL-PWM) in cascaded H-bridge (CHB) converter, however, it will produce narrow- and error-pulse in the process of implementation, which has a negative impact on CHB efficiency, output voltage quality and safe operation of power devices. In this paper, the NL-PWM strategy is improved based on round function to solve the narrow pulse of traditional NL-PWM, and a step wave delay update strategy is proposed to solve the error-pulse of NL-PWM. This paper analyzes the basic principle of improved NL-PWM and its ability to avoid narrow pulse of power device, shows the harmonic characteristics of improved NL-PWM, analyzes the generation mechanism of error pulse, and calculates the error of output voltage caused by the proposed step wave delay update strategy. The correctness of theoretical analysis and the effectiveness of the proposed method are verified by simulation and experiment. The research shows that the improved NL-PWM can effectively prevent narrow pulse while maintaining good harmonic characteristics, and the proposed step wave delay update strategy can avoid error pulse and has little impact on the output of NL-PWM.

Integrating renewable energy sources (RESs) into electrical power systems has gotten highly noticeable among researchers and those interested in electrical energy production due to the increase in energy demands, fossil fuel exhaustion, and ecological effects. PV-based renewable energy generation is one of the essential RESs that has appeared and had played a vital role in electrical power systems recently due to their advantages. In this regard, this paper presents a multi-objective computation problem for optimal siting and the design of grid-tied PV systems to achieve optimum generating reliability, considering some states of different generation probabilities. The proposed paper studies the evaluation of the grid-tied PV systems reliability, the states of generation probabilities, the generation buses availabilities, the capacities of the generation’s system in or out of service for each failure state, and the frequency and mean duration of generation failure states. The presented multi-objective computation problem is optimized using a modified adaptive accelerated particle swarm optimization (MAACPSO) algorithm. The effectiveness of the proposed method is demonstrated through IEEE_EPS_24_bus integrated with PV systems. Results revealed the ability of MAACPSO to solve the multi-objective optimization problem presented, consequently supporting the system reliability.

This paper proposes a non-contact method for fault location in transmission lines, which is based on magnetic fields produced by current signals measured using magnetoresistive sensors installed only at transmission line terminals, under the phase conductors of the first transmission tower at both terminals or at the substations portals. The proposed method uses the Extended Kalman filter to process these measurements and is based on a travelling wave approach in order to perform the fault localization. This paper also describes the implementation and testing of the method, firstly introducing its overview, followed by an analysis of the magnetic fields produced by the current signals, as well as considerations on their measurement; secondly, detailing the Extended Kalman filter and the travelling wave approach; and, finally, presenting the results of the method with regards to simulations built using EMTP/ATP to evaluate its robustness under different conditions varying the fault resistance, fault inception angle, phases involved and fault location. The results indicate that the proposed method is robust and accurate.

Currently, research work is primarily dependent on the collection of large sets of data from systems and making predictions based on the knowledge obtained from the data, which is generally termed as ‘data mining’. These data mining algorithms are of great importance in improving the performance of different applications. In this regard, Machine Learning (ML) algorithms have been demonstrated to be excellent tools to cope with difficult problems. In this paper, a classification learner based supervised ML algorithm is proposed for intentional islanding of DERs based on the live data collected from supervisory control and data acquisition (SCADA) system in post disaster situations. Literature presents various islanding detection techniques and also intentional islanding algorithms to address different problems in AC networks. These algorithms majorly work based on the control of current source or voltage source inverters. On the other hand, a low voltage DC distribution system allowing the removal of inverter is proposed, which is supposed to be more advantageous by reducing losses and is also economical when working with DERs. In this paper, ML based intentional islanding algorithm for DERs based low voltage DC distribution system is proposed by considering the effects of natural disasters. The learner models trained are fine tree, linear SVM, quadratic SVM and Gaussian SVM. The training of fine tree model is achieved with higher accuracy of 99.8%. The main objective of this work is to achieve a faster and accurate decision making. The performance of the ML based intentional islanding algorithm is compared with the earlier proposed artificial intelligence (AI) based intentional islanding algorithms. The AI algorithms proposed earlier are fuzzy inference systems (FIS), artificial neural networks (ANN) and adaptive network based fuzzy inference system (ANFIS). The comparison shows that, the decision making with ML based intentional islanding algorithm is faster and accurate than all other algorithms.

In this paper, an efficient bi-level framework is proposed to enhance the resilience of microgrids (MGs) against islanding due to low probability-high impact events by incorporating battery swapping stations (BSSs). In the emergency condition, MG solves the upper-level of the proposed model to report the desired energy transaction including surplus energy and unsupplied loads during the islanding period to the BSSs coordinator. The lower-level problem will be solved with an iterative algorithm by BSSs coordinator to report different plans of energy transactions and their prices to the MG during the emergency period. The price of each energy transaction plan is determined based on a bonus mechanism. Finally, MG will choose the best plan of energy trading considering a new proposed perspective of resilience improvement. Furthermore, a new formulation for BSS operation with fewer variables in comparison to the previous works is proposed in this paper. Simulations are carried out on an MG with two BSSs to verify the proposed model.

MLD system is a type of hybrid system, which adopts the form of integer linear inequality including continuous variable and logical variable, which reflects both continuous and discrete dynamic characteristics by switching law. In this paper, the problem of optimal management of open laboratories in Colleges and universities is illustrated in the form of a class of advanced MLD system. By analyzing the main factors of laboratory consumption, the laboratory cost function is established. Taking the minimum cost as the objective function, it uses the random number of students in the laboratory to realize the energy-saving optimization management of the laboratory. At the end of this paper, a simulation example is used to analyze the process of MLD system used in laboratory optimization management, which shows hybrid dynamic characteristics, optimal management effect and ideal applicability.

Focusing on the issues of the effects of the high failure rate and poor maintenance on the ZPW-2000A track circuit during in-field operations, this paper proposes a multi-objective maintenance decision optimization method by considering the number of maintenance personnel. The developed method introduces not only the service age regression, increased failure rate, system’s average reliability, maintenance costs, but also the availability calculation model in the case of multiple maintenance personnel. We solve the model using the integer coded BB-MOPSO algorithm, and obtain the Pareto front solution set, and then the optimal maintenance plan of the system is further established. In addition, the number of optimal maintenance personnel is analyzed in detail. Compared to the results of traditional maintenance strategies, the results obtained in this paper show that the proposed model can reduce the maintenance costs, under the same working conditions, by 13.29%, moreover, it can more comprehensively reflect the impact of maintenance activities on the system reliability, maintenance costs, and availability. Furthermore, the method can meet the needs of decision-makers for diversified solutions and can provide an effective decision support while on site.

Dynamic programming (DP) can be used to generate the optimal schedules of a smart home energy management system (SHEMS), however, it is computationally difficult because we have to loop over all the possible states, decisions and outcomes. This paper proposes a novel state-space approximate dynamic programming (SS-ADP) approach to quickly solve a SHEMS problem but with similar solutions as DP. The state-space approximations are made using a hierarchical approach, which involves clustering and machine learning. The proposed SS-ADP can generate the day-ahead value functions quickly without compromising the solution quality because it only loops over the necessary state-space. Our simulation results showed that the solutions from the SS-ADP approach are within 0.8% of the optimal DP solutions but saves the computational time by at least 20%. The paper also presents a fast real-time control strategy under uncertainty using the Bellman optimality condition and long short-term memory recurrent neural networks (LSTM-RNN). The Bellman equation uses the day-ahead value function from the SS-ADP and the instantaneous contribution function to make fast real-time decisions. The instantaneous contribution is calculated using the PV and load predicted using LSTM-RNN, which performs significantly better than the widely used persistence method.

Three-phase unbalance issue can cause adverse impacts on the power quality of the solid-state transformer (SST) based AC/DC hybrid microgrid. In this paper, an unbalanced power control strategy for cascaded dual active bridge (DAB) converter is proposed. With the strategy, the transmitted power of each DAB module in each phase is evenly distributed in terms of the three-phase unbalance degree of the three-phase AC grid. Thus, the idea of unequal power sharing in cascaded DAB can be applied to mitigate the three-phase unbalance from the perspective of power control. Moreover, this paper proposed a three-phase unbalance power mitigation algorithm based on the unbalanced power control strategy for the cascaded DAB. The proposed strategy can help the SST realize the function of mitigating three-phase unbalance when it transmits energy to the DC microgrid. Thus, the proposed control strategy can mitigate the three-phase unbalance on the premise that the power demand of the system is met. Finally, the simulations and the experiment results have verified the effectiveness and correctness of the proposed control method.

With the modernization of power grids, the network optimal utilization is essential to ensure that voltage profile at each bus is maintained within an acceptable range, voltage stability of the system is enhanced, power losses in lines are minimized, reliability and security of system are improved and etc. These can be achieved by introducing reactive power compensation devices such as Flexible Alternating Current Transmission System (FACTS) devices, Custom Power (CP) devices, synchronous condenser, capacitor bank and etc in distribution or transmission networks. Optimal location and sizing of the reactive power compensation devices are significantly important to ensure sufficient investment onto this device. Recently, most of conducted studies had focused on the techniques for determining the optimal location and sizing of various reactive power compensation devices in the power system using various indices proposed in the literature to access the power loss, voltage stability, voltage profile and line loadability. However, no review paper had discussed on the application of the existing indices adopted in the available techniques for solving the optimal location and sizing problems for all types of reactive power compensation devices. In this paper, current literature survey on optimal location and sizing of reactive power compensations had been discussed which includes analytical, conventional, metaheuristic and hybrid based approaches. The main objectives are to reduce power losses, to mitigate voltage deviations, to increase voltage stability and to improve reliability and security of the system.

In this paper, an industrial grade adaptive control scheme is proposed for a micro-grid integrated dual active bridge driven battery management system (DIBMS). A benchmark industrial grade adaptive control scheme depends on two factors namely robustness and computational resource utilization when such controllers are implemented over processors. The mathematical model of DIBMS system is nonlinear, thus for desired response, non-linear controllers based on sliding mode variable structure control theory suits it well for the state regulation problem of DIBMS, however such controllers utilize high computational resources when practically implemented over processors. Keeping in view the above performance indices, this paper proposes an industrial grade computationally efficient and finite time adaptive robust convergent control for DIBMS system. A proportional integral (PI) scheme is used as central control unit and Hebbian algorithm with double integration of the state error is introduced for online tuning the gains of central control unit. The robustness and computational resource efficiency of the proposed control paradigm is validated using a laboratory scale test bench through TI Launchpad (TMS320F28379D). The superiority of the proposed AI based PI control paradigm is compared with classical PI, integer order sliding mode control (SMC), and fractional order SMC (FOSMC) in terms of computational resource utilization and robustness under all test conditions.

This paper deals with the real-time simulation of a power tool battery pack on a mechatronic powertrain test bench. The ability of an easy-to-use model for quick and iterative test runs mainly depends on the effort of parameterization. For this purpose, an easily parameterizable battery model is required. The battery model used is based on the current state of research and simulates the battery’s behavior with an adequate precision. The suggested parameterization allows building the model without the necessity of experimental investigation. Three different procedures for model parameterization were used and compared with the real battery behavior. In conclusion, this paper shows a good tradeoff between precision and an easy way to handle a battery model for testing mechatronic powertrains.

Utility grids are undergoing several upgrades. Distributed generators that are supplied by intermittent renewable energy sources (RES) are being connected to the grids. As RES get cheaper, more customers are opting for peer-to-peer energy interchanges through the smart metering infrastructure. Consequently, power management in grid-tied RES-based microgrids has become a challenging task. This paper reviews the applications of reinforcement learning (RL) algorithms in managing power in grid-tide microgrids. Unlike other optimization methods such as numerical and soft computing techniques, RL does not require an accurate model of the optimization environment in order to arrive at an optimal solution. In this paper, various challenges associated with the control of power in grid-tied microgrids are described. The application of RL techniques in addressing those challenges is reviewed critically. This review identifies the need to improve and scale multi-agent RL methods to enable seamless distributed power dispatch among interconnected microgrids. Finally, the paper gives directions for future research, e.g., the hybridization of intrinsic and extrinsic reward schemes, the use of transfer learning to improve the learning outcomes of RL in complex power systems environments and the deployment of priority-based experience replay in post-disaster microgrid power flow control.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A modular battery-based energy storage system is composed by several battery packs distributed among different modules or parts of a power conversion system (PCS). The design of such PCS can be diverse attending to different criteria such as reliability, efficiency, fault tolerance, compactness and flexibility. The present paper proposes a quantitative and qualitative comparison among the most widely proposed PCSs for modular battery-based energy storage systems in literature. The obtained results confirm the high performance of those PCSs based on the parallel connection of different modules to a single point of common coupling, also identifying those based on modular multilevel cascaded converters as promising concepts according to the assumptions of the present paper.

Small-signal stability problems often occur when the inverter for renewable energy generation is connected to weak grid. A small-signal transfer function integrated model reflecting the interaction of grid impedance, phase locked-loop (PLL), and current control loop is established in this paper. Based on the established model, the oscillation mechanism of the grid-connected inverter system is revealed: the inductance current flowing through the grid impedance can produce a voltage disturbance, which will eventually affect the inductance current through PLL and current control loop. Therefore, the loop composed of the grid impedance and PLL can easily lead to the oscillation of the grid-connected inverter system under weak grid condition. To suppress the oscillation, a control parameters design method of the grid-connected inverter is proposed. Without changing the control method, the proposed control parameters design method can ensure the stable operation of the grid-connected inverter system under the very weak grid condition when the short-circuit ratio (SCR) is 2. Finally, the experimental results verify the correctness of the stability analysis and the effectiveness of the control parameters design method proposed in this paper.

This paper aims to reasonably demonstrate the role of the rotor-side converter (RSC) in subsynchronous oscillation (SSO) and investigates the influence and relationships of all control parameters. Thus, the equivalent circuit of a doubly fed induction generator (DFIG) that considers the RSC is imperative. First, the pertinent equivalent model of a DFIG with RSC control is established. Compared with previous works, this paper innovatively merges the RSC and induction generator with flux and voltage, obtains a complete analytical expression for the equivalent power supply and impedance containing all parameters of both the inner and outer loops of the RSC and then constructs a reasonable equivalent model. The action of the RSC is successfully demonstrated and reflects how the control loops and parameters of the RSC affect the circuit structures and properties of the equivalent model. Then, based on this proposed model, the influence mechanism of the RSC on the SSO of the grid-connected system is carefully analyzed.

Effective early warning of wind turbine failures is of great significance to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. At present, most wind farms are installed with supervisory control and data acquisition (SCADA) system, and SCADA data contains a lot of hidden information, which can be used for fault early warning. This paper uses the generator temperature and gearbox oil temperature in the SCADA data as the entry point for fault warning. Firstly, the eXtreme gradient boosting (XGBoost) algorithm is used to establish the normal temperature regression prediction model of wind turbine components. Then, the residual between the predicted value and the actual value is calculated, and the change trend of the residual is monitored by the principle of exponentially weighted moving-average (EWMA) control chart. Finally, by setting an appropriate threshold, the variation trend of the residual is judged to determine the occurrence and development of the fault. This paper uses two fault detection methods: fixed threshold and dynamic threshold based on adaptive algorithm, and compares the advantages and disadvantages of the two methods. Based on the SCADA data of a wind farm in Inner Mongolia (China), this paper designs the fault early warning test of the wind turbine generator and gearbox. The experimental results show that for the generator, the fixed fault threshold method can give the fault alarm 3 hours in advance, while the dynamic fault threshold determination method can give fault alarm 4.25 hours in advance. For gearbox, the fixed fault threshold method can give the fault alarm 2 hours in advance, while the dynamic threshold fault diagnosis method can send out the fault alarm 2.75 hours in advance.

A new three phase multilevel inverter with reduced number of components count is proposed in this paper. This inverter is designed using a single DC source per phase to generate multiple level output voltage which makes it suitable for low and medium voltage applications, including ac-coupled renewables or energy storages. A generalized circuit configuration is shown in this paper following which the number of output voltage level can be increased as per expectation. Although, each element endures the voltage stress equivalent to the input DC voltage, the value of total standing voltage (TSV) is reduced by the utilization of minimized number of components with respect to the number of series connected capacitors. Further, staircase modulation scheme is used to generate the switching signals. Hence, the proposed inverter can be operated at low switching frequency with optimal output current harmonic distortion which decreases switching losses and suppresses power factor falling. In order to validate the theoretical explanations and practical performances of the proposed inverter, the hypothesis is simulated for 9, 13 and 39 output voltage level inverters for three phase with a line voltage total harmonic distortion (THD) of 6.06%, 4.16% and 2.10% respectively in MATLAB/Simulink and a 5-level single phase laboratory prototype is implemented in the laboratory.

© 2013 IEEE. This paper presents a multi-objective dynamic economic dispatch model with renewable obligation requirements. The two objective functions that are presented in this paper aim to increase the level of renewable energy sources in the grid while minimising the total operating cost and respecting the spinning reserves required to maintain continuity of supply. The proposed model incorporates thermal power plants, photovoltaic and wind power plants into the grid. The paper presents a Pareto optimal solution which is a compromise between maximising the renewable energy source generation while minimising the operating costs. A renewable obligation policy is implemented in the proposed model to ensure that renewable energy source generators are utilised and any failure to attain the required renewable obligation is penalised in line with the renewable obligation framework. The proposed model is modified into a single objective optimisation problem and numerical tests are performed on the modified IEEE 24 RTS system and IEEE 118 bus system to test the effectiveness of the model. A comparative study evaluates the impact of the proposed model on the traditional dynamic economic dispatch in terms of the achieved renewable energy source penetration level and economic performance. The numerical simulations show that the proposed model is robust and can attain high renewable energy source penetration levels.

The concept of virtual synchronous machine (VSM) was proposed to deal with the shortcomings of low inertia and damping of traditional control strategies for power electronic converters. But what if all distributed energy resources and controllable loads in a microgrid adopt the VSM control strategy, and will it present better performance than conventional droop control-based microgrid (DMG)? In this paper, the VSM-based microgrid (VSMG) is analyzed. The small-signal modeling of the VSMG is studied at first. Then static stability and dynamic characteristics of the VSMG are analyzed and compared with the DMG in both frequency-domain and time-domain. With the growing scale of microgrids, their modeling and simulation are becoming significant computational burdens. Inspired by the participation factor analysis of the VSMG and the concept of coherency in power systems, the VSMG small-signal model is equivalent to a modified third-order synchronous generator (SG) model in this paper. The equivalencing involves gray-box system identification and is realized by estimating equivalent electrical parameters alternately and iteratively. The equivalent SG (EqSG) model is compared with three representative model order reduction methods to verify its effectiveness. Simulation results confirm the accuracy of the EqSG model substituting detailed VSMG model in time-domain simulations.

With the rapid development of industrialization, the proportion of electric arc furnaces (EAF) in distribution networks is getting higher and higher. Aiming at the problem that grid voltage is distorted by a large number of arc furnace nonlinear loads accessing to distribution networks, it is crucial to make dynamic analysis of distribution network voltage and adopt a control strategy of grid-connected converter based on the new phase-locked loop (PLL) technology. As a result, the harmonic distortion rate is reduced and the quality of grid-connected current and voltage is improved. In this paper, photovoltaic (PV) system model and the typical dynamic model of the EAF are established. By analyzing the influence of the EAF model on the PV grid-connected converter with the traditional phase-locked loop while connected to distribution network, a control strategy of the PV grid-connected converter with the self-adjusting double SOGI (MAF-SASOGI) phase-locked loop with the ideal low-pass filter is proposed. Through the simulation analysis, it is verified that precise phase locked can be realized and harmonic content of the system can be reduced by the PLL strategy proposed in this paper. Therefore, the quality of PV grid-connected voltage and current is improved.

Wind energy is one of the premier renewable energy sources that have gained popularity during the last decade. Among the currently available wind energy conversion systems (WECS), doubly fed induction generator-based technology has been widely employed due to its superior advantageous. The key features of the DFIG-based WECS include its ability to capture more wind energy and support the grid with large reactive power during short disturbance events. On the other hand, DFIG is very sensitive to grid faults which affect its fault ride through (FRT) capability. Furthermore, extreme wind gust even for short durations may lead to the violation of the low voltage ride through (LVRT) threshold limits set by worldwide transmission line operators. This situation cannot be mitigated by the turbine blades pitch controller since the response of the pitch mechanical control is much slower than the rapid dynamic change in the wind speed during short duration of wind gust events. While DFIG FRT capability has been discussed in several papers in the literature, not much attention was given to the mitigation of the effects of extreme wind gust of short duration on the DFIG performance. In this paper, the effect of various levels of wind gust on the performance of a DFIG-based wind energy conversion grid-connected system is investigated and mitigated using a new controller for superconducting magnetic energy storage (SMES) unit. A combination of hysteresis current and fuzzy logic controllers is employed to control the voltage source converter and the DC-DC chopper interfacing the SMES coil with the investigated system. Simulation results reveal the effectiveness of the proposed SMES controller that can be easily implemented within existing as well as new WECS installations.

This paper presents a novel data-driven approach for predicting lightning-related outages that occur in power distribution systems on a daily basis. In order to develop an approach that is able to successfully fulfill this objective, there are two main challenges that ought to be addressed. The first challenge is to define the extent of the target area. An unsupervised machine learning approach is proposed to overcome this difficulty. The second challenge is to adequately identify characteristics of lightning-related outages and to explore the relationship between these outages and weather-related variables (thunderstorm events). In this paper, these outages are clustered into a few manageable groups. Then, a probabilistic model is presented to estimate the likelihood of each group of outages. Finally, a machine learning classification algorithm that can handle the imbalanced problem is developed to predict what group will the outage belong to on a specific day in a specific area of the system under study. Actual outage data, obtained from a major utility in the U.S., in addition to radar weather forecast data are utilized to build the proposed approach. Also, three case studies are provided to show several issues associated with predicting lightning-related outages, and to demonstrate how the proposed approach can address those problems adequately.

A significant component in electrical power systems are substations, in which the substation communication network is essential. Due to the evolving nature of communication protocols, and the introduction of the IEC 61 850 standard, substation communication networks can provide fast and reliable information transmission. Such improvements in transmission allow for improved protection and control of the power system network. A well-designed substation communication network can be achieved with the use of appropriate modeling, and as a result, this paper aims the review the methods in which parts of, or the entire substation communication network can be modeled. Modeling methods aim to determine various parameters of a substation communication network design, which include the traffic load distribution, and various message delays under different network conditions that may be experienced. In order for modeling to be carried out, a general structure of a substation communication network must be defined, which is laid out by the IEC 61 850 standard. The findings of the review in this paper show that there are various methods in which a substation communication network can be modeled, which include analytical modeling, software-based modeling, hardware/experimental modeling, and time-series based modeling. However, each method enables for different parameters to be found, with some of the methods having drawbacks including a limited network model size, and inability to find certain necessary parameters. The methods reviewed in this paper can be used and expanded on to create useful and accurate models of a substation communication network.

This work is focused on a step-down switched-inductor hybrid dc-dc converter (SIHDC) integrated in a small power wind energy conversion system (WECS). The converter has two roles, to maintain the wind turbine at the maximum power point by controlling the electric generator loading, and to charge a high power density supercapacitor, which is part of a hybrid storage unit that also contains rechargeable batteries. The paper points out the steady-state analysis of the converter in continuous and discontinuous current modes, specific for this application. The stability investigation is presented in detail, including the influence of the electrical generator. The parameterized small-signal transfer function of the control variable derived in this paper can be used to quickly obtain the information needed to evaluate the stability of any similar WECS. A current controller was designed to achieve a stable operation in continuous current mode, and afterwards the stability was checked for discontinuous current mode. A 5kW step-down SIHDC prototype was built and extensively tested, both in laboratory, by using a wind turbine emulator, and in a small power (5kW) WECS industrial platform. Experimental results obtained under real operating conditions confirm the theoretical analysis and laboratory tests, showing that SIHDC converter is a valid solution for low power wind energy conversion systems.

One of the important tasks for increasing the efficiency of photovoltaic (PV) system is the development and improvement of the maximum power point tracking algorithms (MPPT). These MPPT algorithms lead to the ability to catch efficiently the global maximum power point of the partially shaded PV array. One of these trackers is the particle swarm optimization (PSO) algorithm which is one of the Soft computing techniques. The conventional PSO based trackers have many advantages such as the simplicity of hardware implementation and independence from the installed system. The actual problem of the practical application of PSO is the determination of its parameters to ensure high effectiveness of extracting the global MPP. Analysis of scientific papers devoted to the PSO algorithm has shown that there is currently no methodology for the optimal parameters' selection of PSO algorithm based maximum power trackers for the PV system. This paper aims to create a convenient and reasonable method for choosing the optimal parameters of the PSO algorithm, taking into account the topology and parameters of the DC-DC converter and the configuration of solar panels. A new method for selecting the parameters of a buck converter connected to a battery has been presented. The optimal value of the sampling time for the digital MPP controllers, providing their maximum performance; has been determined based on a new methodology. Matlab/Simulink software package is used as the main research tool. The prominent outcomes identify that the modified PSO and its designed parameters best meet the requirements of the MPPT controller for the PV system.

MMC-HVdc transmission system has become the mainstream of power construction and plays an increasingly important role in the power system. However, the dc line fault results in shutting down of the converters, which can greatly endanger the security of the power grid. So it is essential to find a fast fault identification strategy for dc line protection technology. Besides, the existing backup protection scheme has the problems of long delay and strict communication synchronization requirements. Therefore, this paper proposes a novel protection scheme for mmc-HVdc transmission lines based on the Cross-Entropy of charge. The fault current oscillation phenomenon and fault current frequency characteristics of mmc-HVdc transmission lines are analyzed first. Secondly, this paper proposes a method to filter out distributed capacitance current by charge and eliminate the current oscillation. Finally, the difference of the charge during different faults (internal fault and external fault) is analyzed, and the Cross-Entropy is introduced to express this difference. Hence, this paper proposes a new principle of identifying faults using the Cross-Entropy of the charge. Simulation results show that the protection principle is not affected by the distributed capacitive current and does not require strict communication synchronization. It has excellent performance in intolerance to fault resistance and noise interference and can be used as backup protection for the mmc-HVdc transmission system.

With the increase of distributed generation penetration, the economic operation of microgrid has been fully developed. For islanded microgrid, due to the lack of large grid support and the limited capacity of the distributed generation (DG), the energy supply of microgrid may not meet the need of users in real time. Therefore, it is of great significance to solve the problem of insufficient power supply of islanded microgrid and improve the economy of its operation. Firstly, this paper analyses the frequency characteristics of DG, and establishes power models oriented to the frequency regulation characteristics of microgrid and introduces the mathematical model of DG. Then, an islanded microgrid day-ahead optimization scheduling model considering frequency regulation characteristics of units and demand response (DR) is proposed. The frequency characteristics of controllable units and DR are considered in the model, and different strategies are selected to analyze their impact on microgrid economy. The example shows that the optimization scheduling model of microgrid established in this paper can reduce the power supply pressure during peak load periods, alleviate the problem of insufficient power supply, and effectively improve the overall benefit of microgrid, which verifies the rationality of the model.

This paper proposes a Resistance perturbation based maximum power point tracking (MPPT) with an adaptive control limit algorithm to extract the maximum power from solar photovoltaic (PV) array. This algorithm consists of two main functions, namely 1) resistance perturbation & observation (RP&O) and 2) adaptive resistance control (ARC) limit. The RP&O operates the PV array at maximum power point (MPP), and the ARC limit continuously monitors the resistance of the PV Rpv to determine the operating limit of MPP. The ultimate aim of proposing this algorithm is to reduce the oscillations and improve MPP's tracking performance for sudden variation in temperature and irradiance conditions. Furthermore, it does not require an expensive pyranometer or temperature sensor to track the MPP of the PV array. This paper also compares the proposed and conventional MPPT algorithm's performance. Its validation results in both MATLAB/Simulink and experimental studies are presented under constant and sudden changes in irradiance conditions.

The existing path tracking methods usually neglect the effect of the drivers on the steering control. This paper proposes a robust steering control method of human-machine steering torque superposition based on linear matrix inequality (LMI) algorithm. First, the model for solving steering superposition torque introduces the steering system and steering resistance torque model in addition to the vehicle model, which increases the nonlinearity and uncertainty of system, and the human in torque superposition control also increases the external interferences. Therefore, this paper proposes a LMI robust control algorithm to reduce the external interference and the influence of uncertain factors on the system and improve the tracking performance of system, by use of Lyapunov stability theory and Schur complement property to convert the region pole assignment and robust control constraint conditions into LMI convex optimization problem. The next, the nonlinear vehicle dynamics solving model including Fiala tire model, steering column model is established; the nonlinear tire model is linearized by use of affine function, and the steering superposition control law is solved by use of LMI. Then, the union CarSim and Simulink simulation is conducted under different situations to verify the robustness and control performance of control system. Finally, through establishing the hardware-in-the-loop experiment table based on LabVIEW-RT, the effectiveness of control strategy is verified. The test results show that the method solves the model uncertainty and the robustness decrement problem resulting from human intervention, ensuring a good tracking performance, and a stable system at the same time.